Results 41 to 50 of about 228,260 (299)

Deep Learning IP Network Representations [PDF]

Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, 2018
We present DIP, a deep learning based framework to learn structural properties of the Internet, such as node clustering or distance between nodes. Existing embedding-based approaches use linear algorithms on a single source of data, such as latency or hop count information, to approximate the position of a node in the Internet.
Mingda Li, Cristian Lumezanu, Bo Zong, Haifeng Chen   +3 more
openaire   +1 more source

Multiple Kernel Representation Learning on Networks [PDF]

IEEE Transactions on Knowledge and Data Engineering, 2022
This manuscript is an extended version of the previous work entitled "Kernel Node Embeddings" (arXiv:1909.03416), and it has been accepted for publication in IEEE Transactions on Knowledge and Data ...
Abdulkadir Çelikkanat, Yanning Shen, Fragkiskos D. Malliaros   +2 more
openaire   +3 more sources

A Hybrid Deep Network Representation Model for Detecting Researchers’ Communities [PDF]

Journal of Artificial Intelligence and Data Mining, 2022
Recently, network representation has attracted many research works mostly concentrating on representing of nodes in a dense low-dimensional vector. There exist some network embedding methods focusing only on the node structure and some others considering
A. Torkaman, K. Badie, A. Salajegheh, M. H. Bokaei, Seyed F. Fatemi   +4 more
doaj   +1 more source

Integrating Social Circles and Network Representation Learning for Item Recommendation [PDF]

, 2019
With the increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms.
Wang, Can, Sifan Wu, Zhang, Li, Yonghong Yu, Wang, Qiang, Boyu Qi, Xiaotian Wu, Wang, C, Li Zhang, Wu, Sifan, Qi, Boyu, Wu, Xiaotian, Qi, B, Yu, Y, Zhang, L, Wu, X, Wang, Q, Yu, Yonghong, Qiang Wang, Wu, S, Can Wang   +20 more
core   +1 more source

Hypernetwork Representation Learning with the Set Constraint

Applied Sciences, 2022
There are lots of situations that cannot be described by traditional networks but can be described perfectly by the hypernetwork in the real world. Different from the traditional network, the hypernetwork structure is more complex and poses a great ...
Yu Zhu, Haixing Zhao
doaj   +1 more source

Deep Network Representation Learning Method on Incomplete Information Networks [PDF]

Jisuanji kexue, 2021
The goal of network representation learning(NRL) is embedding network nodes into low-dimensional vector space,for effective feature representation of the downstream tasks.Due to the difficulty of information collection in the real-world scene-ries,large ...
FU Kun, ZHAO Xiao-meng, FU Zi-tong, GAO Jin-hui, MA Hao-ran
doaj   +1 more source

Representation Learning for Scale-Free Networks

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Network embedding aims to learn the low-dimensional representations of vertexes in a network, while structure and inherent properties of the network is preserved. Existing network embedding works primarily focus on preserving the microscopic structure, such as the first- and second-order proximity of vertexes, while the macroscopic ...
Rui Feng, Yang Yang 0009, Wenjie Hu 0003, Fei Wu 0001, Yueting Zhuang   +4 more
openaire   +2 more sources

Scattering Networks for Hybrid Representation Learning [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
arXiv admin note: substantial text overlap with arXiv:1703 ...
Zagoruyko, S., Huang G., Komodakis, N., Lacoste-Julien, S., Blaschko M., and Belilovsky E.   +8 more
openaire   +4 more sources

Dynamic Influence Maximization via Network Representation Learning

Frontiers in Physics, 2022
Influence maximization is a hot research topic in the social computing field and has gained tremendous studies motivated by its wild application scenarios.
Wei Sheng, Wenbo Song, Dong Li, Fei Yang, Yatao Zhang   +4 more
doaj   +1 more source

Deep Inductive Network Representation Learning [PDF]

Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18, 2018
This paper presents a general inductive graph representation learning framework called DeepGL for learning deep node and edge features that generalize across-networks. In particular, DeepGL begins by deriving a set of base features from the graph (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where ...
Ryan A. Rossi, Rong Zhou 0001, Nesreen K. Ahmed   +2 more
openaire   +1 more source